Algebraic and Topological Indices of Molecular Pathway Networks in Human Cancers

TL;DR

This study analyzes protein-protein interaction networks in 11 human cancers using algebraic and topological indices, revealing correlations with survival rates and identifying key protein families as potential drug targets.

Contribution

It introduces a novel framework linking network symmetry and complexity to cancer prognosis, aiding in target identification and cross-disease network analysis.

Findings

Strong correlation between network automorphism sizes and survival probabilities

Identification of recurrent protein families as central motifs in cancer pathways

Symmetry sources are often central, not peripheral, in networks

Abstract

Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).